". . . a goldmine of knowledge on accelerated life testingprinciples and practices . . . one of the very few capable ofadvancing the science of reliability. It definitely belongs inevery bookshelf on engineering."
–Dev G. Raheja, Quality and Reliability EngineeringInternational
". . . an impressive book. The width and number of topicscovered, the practical data sets included, the obvious knowledgeand understanding of the author and the extent of publishedmaterials reviewed combine to ensure that this will be a book usedfrequently."
–Journal of the Royal Statistical Society
A benchmark text in the field, Accelerated Testing: StatisticalModels, Test Plans, and Data Analysis offers engineers, scientists,and statisticians a reliable resource on the effective use ofaccelerated life testing to measure and improve productreliability. From simple data plots to advanced computer programs,the text features a wealth of practical applications and a clear,readable style that makes even complicated physical and statisticalconcepts uniquely accessible. A detailed index adds to its value asa reference source.
Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package.
The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.
The use of periodically correlated (or cyclostationary)processes has become increasingly popular in a range of researchareas such as meteorology, climate, communications, economics, andmachine diagnostics. Periodically Correlated Random Sequencespresents the main ideas of these processes through the use of basicdefinitions along with motivating, insightful, and illustrativeexamples. Extensive coverage of key concepts is provided, includingsecond-order theory, Hilbert spaces, Fourier theory, and thespectral theory of harmonizable sequences. The authors also providea paradigm for nonparametric time series analysis including testsfor the presence of PC structures.
Features of the book include:
An emphasis on the link between the spectral theory of unitaryoperators and the correlation structure of PC sequences
A discussion of the issues relating to nonparametric time seriesanalysis for PC sequences, including estimation of the mean,correlation, and spectrum
A balanced blend of historical background with modernapplication-specific references to periodically correlatedprocesses
An accompanying Web site that features additional exercises aswell as data sets and programs written in MATLAB® forperforming time series analysis on data that may have a PCstructure
Periodically Correlated Random Sequences is an ideal text ontime series analysis for graduate-level statistics and engineeringstudents who have previous experience in second-order stochasticprocesses (Hilbert space), vector spaces, random processes, andprobability. This book also serves as a valuable reference forresearch statisticians and practitioners in areas of probabilityand statistics such as time series analysis, stochastic processes,and prediction theory.
Missing Data in Clinical Studies has been developedthrough a series of courses and lectures. Its practical approachwill appeal to applied statisticians and biomedical researchers, inparticular those in the biopharmaceutical industry, medical andpublic health organisations. Graduate students of biostatisticswill also find much of benefit.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.
New to the Third Edition
Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
"An indispensable addition to any serious collection on lifetimedata analysis and . . . a valuable contribution to the statisticalliterature. Highly recommended . . ."
"This is an important book, which will appeal to statisticiansworking on survival analysis problems."
"A thorough, unified treatment of statistical models and methodsused in the analysis of lifetime data . . . this is a highlycompetent and agreeable statistical textbook."
-Statistics in Medicine
The statistical analysis of lifetime or response time data is a keytool in engineering, medicine, and many other scientific andtechnological areas. This book provides a unified treatment of themodels and statistical methods used to analyze lifetime data.
Equally useful as a reference for individuals interested in theanalysis of lifetime data and as a text for advanced students,Statistical Models and Methods for Lifetime Data, Second Editionprovides broad coverage of the area without concentrating on anysingle field of application. Extensive illustrations and examplesdrawn from engineering and the biomedical sciences provide readerswith a clear understanding of key concepts.
New and expanded coverage in this edition includes:
* Observation schemes for lifetime data
* Multiple failure modes
* Counting process-martingale tools
* Both special lifetime data and general optimizationsoftware
* Mixture models
* Treatment of interval-censored and truncated data
* Multivariate lifetimes and event history models
* Resampling and simulation methodology
Sampling of Populations, Fourth Edition continues toserve as an all-inclusive resource on the basic and most currentpractices in population sampling. Maintaining the clear andaccessible style of the previous edition, this book outlines theessential statistical methodsfor survey design and analysis, whilealso exploring techniques that have developed over the pastdecade.
The Fourth Edition successfully guides the reader throughthe basic concepts and procedures that accompany real-world samplesurveys, such as sampling designs, problems of missing data,statistical analysis of multistage sampling data, and nonresponseand poststratification adjustment procedures. Rather than employ aheavily mathematical approach, the authors present illustrativeexamples that demonstrate the rationale behind common steps in thesampling process, from creating effective surveys to analyzingcollected data. Along with established methods, modern topics aretreated through the book's new features, which include:A new chapter on telephone sampling, with coverage of decliningresponse rates, the creation of "do not call" lists, and thegrowing use of cellular phonesA new chapter on sample weighting that focuses on adjustmentsto weight for nonresponse, frame deficiencies, and the effects ofestimator instabilityAn updated discussion of sample survey data analysis thatincludes analytic procedures for estimation and hypothesistestingA new section on Chromy's widely used method of takingprobability proportional to size samples with minimum replacementof primary sampling unitsAn expanded index with references on the latest research in thefield
All of the book's examples and exercises can be easily workedout using various software packages including SAS, STATA, andSUDAAN, and an extensive FTP site contains additional data sets.With its comprehensive presentation and wealth of relevantexamples, Sampling of Populations, Fourth Edition is anideal book for courses on survey sampling at theupper-undergraduate and graduate levels. It is also a valuablereference for practicing statisticians who would like to refreshtheir knowledge of sampling techniques.
This publication provides a complete and integrated presentation ofthe fields of fractals and point processes, from definitions andmeasures to analysis and estimation. The authors skillfullydemonstrate how fractal-based point processes, established as theintersection of these two fields, are tremendously useful forrepresenting and describing a wide variety of diverse phenomena inthe physical and biological sciences. Topics range frominformation-packet arrivals on a computer network toaction-potential occurrences in a neural preparation.
The authors begin with concrete and key examples of fractals andpoint processes, followed by an introduction to fractals and chaos.Point processes are defined, and a collection of characterizingmeasures are presented. With the concepts of fractals and pointprocesses thoroughly explored, the authors move on to integrate thetwo fields of study. Mathematical formulations for severalimportant fractal-based point-process families are provided, aswell as an explanation of how various operations modify suchprocesses. The authors also examine analysis and estimationtechniques suitable for these processes. Finally, computer networktraffic, an important application used to illustrate the variousapproaches and models set forth in earlier chapters, isdiscussed.
Throughout the presentation, readers are exposed to a number ofimportant applications that are examined with the aid of a set ofpoint processes drawn from biological signals and computer networktraffic. Problems are provided at the end of each chapter allowingreaders to put their newfound knowledge into practice, and allsolutions are provided in an appendix. An accompanying Web sitefeatures links to supplementary materials and tools to assist withdata analysis and simulation.
With its focus on applications and numerous solved problem sets,this is an excellent graduate-level text for courses in suchdiverse fields as statistics, physics, engineering, computerscience, psychology, and neuroscience.
Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author’s package MICE is included throughout the book.
Assuming familiarity with basic statistical concepts and multivariate methods, Flexible Imputation of Missing Data is intended for two audiences:
(Bio)statisticians, epidemiologists, and methodologists in the social and health sciences Substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes
This graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.
This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed examples and interpretation of the presented results as well as exercises throughout
Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
Sparked by the growing interest in statistical methods for theanalysis of spatially referenced data in the field of publichealth, Applied Spatial Statistics for Public Health Datafills the need for an introductory, application-oriented text onthis timely subject. Written for practicing public healthresearchers as well as graduate students in related fields, thetext provides a thorough introduction to basic concepts and methodsin applied spatial statistics as well as a detailed treatment ofsome of the more recent methods in spatial statistics useful forpublic health studies that have not been previously coveredelsewhere.
Assuming minimal knowledge of spatial statistics, the authorsprovide important statistical approaches for assessing suchquestions as:Are newly occurring cases of a disease "clustered" inspace?Do the cases cluster around suspected sources of increasedrisk, such as toxic waste sites or other environmentalhazards?How do we take monitored pollution concentrations measured atspecific locations and interpolate them to locations where nomeasurements were taken?How do we quantify associations between local disease rates andlocal exposures?After reviewing traditional statistical methods used in publichealth research, the text provides an overview of the basicfeatures of spatial data, illustrates various geographic mappingand visualization tools, and describes the sources of publiclyavailable spatial data that might be useful in public healthapplications.
* Organized by problem rather than method, so it guides readers tothe correct technique for solving the problem at hand.
Complete with updates that capture developments from the pastdecade, The EM Algorithm and Extensions, Second Editionsuccessfully provides a basic understanding of the EM algorithm bydescribing its inception, implementation, and applicability innumerous statistical contexts. In conjunction with the fundamentalsof the topic, the authors discuss convergence issues andcomputation of standard errors, and, in addition, unveil manyparallels and connections between the EM algorithm and Markov chainMonte Carlo algorithms. Thorough discussions on the complexitiesand drawbacks that arise from the basic EM algorithm, such as slowconvergence and lack of an in-built procedure to compute thecovariance matrix of parameter estimates, are also presented.
While the general philosophy of the First Edition has beenmaintained, this timely new edition has been updated, revised, andexpanded to include:
New chapters on Monte Carlo versions of the EM algorithm andgeneralizations of the EM algorithm
New results on convergence, including convergence of the EMalgorithm in constrained parameter spaces
Expanded discussion of standard error computation methods, suchas methods for categorical data and methods based on numericaldifferentiation
Coverage of the interval EM, which locates all stationary pointsin a designated region of the parameter space
Exploration of the EM algorithm's relationship with the Gibbssampler and other Markov chain Monte Carlo methods
Plentiful pedagogical elements—chapter introductions,lists of examples, author and subject indices, computer-drawngraphics, and a related Web site
The EM Algorithm and Extensions, Second Edition serves as anexcellent text for graduate-level statistics students and is also acomprehensive resource for theoreticians, practitioners, andresearchers in the social and physical sciences who would like toextend their knowledge of the EM algorithm.
The Wiley-Interscience Paperback Series consists ofselected books that have been made more accessible to consumers inan effort to increase global appeal and general circulation. Withthese new unabridged softcover volumes, Wiley hopes to extend thelives of these works by making them available to future generationsof statisticians, mathematicians, and scientists.
". . .Variance Components is an excellent book. It isorganized and well written, and provides many references to avariety of topics. I recommend it to anyone with interest in linearmodels."
—Journal of the American StatisticalAssociation
"This book provides a broad coverage of methods for estimatingvariance components which appeal to students and research workers .. . The authors make an outstanding contribution to teaching andresearch in the field of variance component estimation."
"The authors have done an excellent job in collecting materialson a broad range of topics. Readers will indeed gain from usingthis book . . . I must say that the authors have done a commendablejob in their scholarly presentation."
This book focuses on summarizing the variability of statisticaldata known as the analysis of variance table. Penned in a readablestyle, it provides an up-to-date treatment of research in the area.The book begins with the history of analysis of variance andcontinues with discussions of balanced data, analysis of variancefor unbalanced data, predictions of random variables, hierarchicalmodels and Bayesian estimation, binary and discrete data, and thedispersion mean model.
Estimation in Surveys with Nonresponse provides anoverview of these techniques, presenting the view of nonresponse asa normal (albeit undesirable) feature of a sample survey, one whosepotentially harmful effects are to be minimised.Builds in the nonresponse feature of survey data collection asan integral part of the theory, both for point estimation and forvariance estimation.Promotes weighting through calibration as a new and powerfultechnique for surveys with nonresponse.Highlights the analysis of nonresponse bias in estimates andmethods to minimize this bias.Includes computational tools to help identify the bestvariables for calibration.Discusses the use of imputation as a complement to weighting bycalibration.Contains guidelines for dealing with frame imperfections andcoverage errors.Features worked examples throughout the text, using realdata.
The accessible style of Estimation in Surveys withNonresponse will make this an invaluable tool for surveymethodologists in national statistics agencies and private surveyagencies. Researchers, teachers, and students of statistics, socialsciences and economics will benefit from the clear presentation andnumerous examples.
This book combines much of what is known in regard to the theory of estimation for semiparametric models with missing data in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is at a level that is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
Thoroughly updated and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fifth Edition presents the statistical principles and processes involved in the analysis of the probabilistic nature of queues. Rather than focus narrowly on a particular application area, the authors illustrate the theory in practice across a range of fields, from computer science and various engineering disciplines to business and operations research. Critically, the text also provides a numerical approach to understanding and making estimations with queueing theory and provides comprehensive coverage of both simple and advanced queueing models. As with all preceding editions, this latest update of the classic text features a unique blend of the theoretical and timely real-world applications. The introductory section has been reorganized with expanded coverage of qualitative/non-mathematical approaches to queueing theory, including a high-level description of queues in everyday life. New sections on non-stationary fluid queues, fairness in queueing, and Little’s Law have been added, as has expanded coverage of stochastic processes, including the Poisson process and Markov chains.
• Each chapter provides a self-contained presentation of key concepts and formulas, to allow readers to focus independently on topics relevant to their interests
• A summary table at the end of the book outlines the queues that have been discussed and the types of results that have been obtained for each queue
• Examples from a range of disciplines highlight practical issues often encountered when applying the theory to real-world problems
• A companion website features QtsPlus, an Excel-based software platform that provides computer-based solutions for most queueing models presented in the book.
Featuring chapter-end exercises and problems—all of which have been classroom-tested and refined by the authors in advanced undergraduate and graduate-level courses—Fundamentals of Queueing Theory, Fifth Edition is an ideal textbook for courses in applied mathematics, queueing theory, probability and statistics, and stochastic processes. This book is also a valuable reference for practitioners in applied mathematics, operations research, engineering, and industrial engineering.
Using current research and discussion of the topic along withclear applications, Modern Experimental Design highlightsthe guiding role of statistical principles in experimental designconstruction. This text can serve as both an applied introductionas well as a concise review of the essential types of experimentaldesigns and their applications.
Topical coverage includes designs containing one or multiplefactors, designs with at least one blocking factor, split-unitdesigns and their variations as well as supersaturated andPlackett-Burman designs. In addition, the text contains extensivetreatment of:Conditional effects analysis as a proposed general method ofanalysisMultiresponse optimizationSpace-filling designs, including Latin hypercube and uniformdesignsRestricted regions of operability and debarredobservationsAnalysis of Means (ANOM) used to analyze data from varioustypes of designsThe application of available software, including Design-Expert,JMP, and MINITAB
This text provides thorough coverage of the topic while alsointroducing the reader to new approaches. Using a large number ofreferences with detailed analyses of datasets, ModernExperimental Design works as a well-rounded learning tool forbeginners as well as a valuable resource for practitioners.
"...this is an excellent book which could easily be used as acourse text..."
—International Statistical Institute
The Fourth Edition of Applied LinearRegression provides a thorough update of the basic theoryand methodology of linear regression modeling. Demonstrating thepractical applications of linear regression analysis techniques,the Fourth Edition uses interesting, real-worldexercises and examples.
Stressing central concepts such as model building, understandingparameters, assessing fit and reliability, and drawing conclusions,the new edition illustrates how to develop estimation, confidence,and testing procedures primarily through the use of least squaresregression. While maintaining the accessible appeal of eachprevious edition,Applied Linear Regression, FourthEdition features:Graphical methods stressed in the initial exploratory phase,analysis phase, and summarization phase of an analysisIn-depth coverage of parameter estimates in both simple andcomplex models, transformations, and regression diagnosticsNewly added material on topics including testing, ANOVA, andvariance assumptionsUpdated methodology, such as bootstrapping, cross-validationbinomial and Poisson regression, and modern model selectionmethods
Applied Linear Regression, Fourth Edition is anexcellent textbook for upper-undergraduate and graduate-levelstudents, as well as an appropriate reference guide forpractitioners and applied statisticians in engineering, businessadministration, economics, and the social sciences.
"If one is involved, or heavily interested, in experiments onmixtures of ingredients, one must obtain this book. It is, as wasthe first edition, the definitive work."
-Short Book Reviews (Publication of the International StatisticalInstitute)
"The text contains many examples with worked solutions and with itsextensive coverage of the subject matter will prove invaluable tothose in the industrial and educational sectors whose work involvesthe design and analysis of mixture experiments."
-Journal of the Royal Statistical Society
"The author has done a great job in presenting the vitalinformation on experiments with mixtures in a lucid and readablestyle. . . . A very informative, interesting, and useful book on animportant statistical topic."
-Zentralblatt fur Mathematik und Ihre Grenzgebiete
Experiments with Mixtures shows researchers and students how todesign and set up mixture experiments, then analyze the data anddraw inferences from the results. Virtually every technique thathas appeared in the literature of mixtures can be found here, andcomputing formulas for each method are provided with completelyworked examples. Almost all of the numerical examples are takenfrom real experiments. Coverage begins with Scheffe latticedesigns, introducing the use of independent variables, and endswith the most current methods. New material includes:
* Multiple response cases
* Residuals and least-squares estimates
* Categories of components: Mixtures of mixtures
* Fixed as well as variable values for the major componentproportions
* Leverage and the Hat Matrix
* Fitting a slack-variable model
* Estimating components of variances in a mixed model using ANOVAtable entries
* Clarification of blocking mates and choice of mates
* Optimizing several responses simultaneously
* Biplots for multiple responses
—American Journal of Psychiatry
In the two decades since the second edition of StatisticalMethods for Rates and Proportions was published, evolvingtechnologies and new methodologies have significantly changed theway today’s statistics are viewed and handled. The explosivedevelopment of personal computing and statistical software hasfacilitated the sophisticated analysis of data, puttingcapabilities that were once the domain of specialists into thehands of every researcher.
The Third Edition of this important text addresses thesechanges and brings the literature up to date. While the previousedition focused on the use of desktop and handheld calculators, thenew edition takes full advantage of modern computing power withoutlosing the elegant simplicity that made the text so popular withstudents and practitioners alike. In authoritative yet clearterminology, the authors have brought the science of data analysisup to date without compromising its accessibility.
Features of the Third Edition include:New material on sample size calculations and issues in clinicaltrials, and entirely new chapters on single-sample data, logisticregression, Poisson regression, regression models for matchedsamples, the analysis of correlated binary data, and methods foranalyzing fourfold tables with missing dataThe addition of many new problems, both numerical andtheoreticalAnswer sections for numerical problems and hints for tacklingthe theoretical onesA frequentist approach enhanced by the inclusion of empiricalBayesian methodology where appropriate
Combining the latest research with the original studies thatestablished the previous editions as leaders in the field,Statistical Methods for Rates and Proportions, Third Editionwill continue to be an invaluable resource for students,statisticians, biostatisticians, and epidemiologists.
Regression with Social Data: Modeling Continuous and LimitedResponse Variables represents the most complete and fullyintegrated coverage of regression modeling currently available forgraduate-level behavioral science students and practitioners.Covering techniques that span the full spectrum of levels ofmeasurement for both continuous and limited response variables, andusing examples taken from such disciplines as sociology,psychology, political science, and public health, the authorsucceeds in demystifying an academically rigorous subject andmaking it accessible to a wider audience.
Content includes coverage of:Logit, probit, scobit, truncated, and censored regressionsMultiple regression with ANOVA and ANCOVA modelsBinary and multinomial response modelsPoisson, negative binomial, and other regression models forevent-count dataSurvival analysis using multistate, multiepisode, andinterval-censored survival models
Concepts are reinforced throughout with numerous chapterproblems, exercises, and real data sets. Step-by-step solutionsplus an appendix of mathematical tutorials make even complexproblems accessible to readers with only moderate math skills. Thebook’s logical flow, wide applicability, and uniquelycomprehensive coverage make it both an ideal text for a variety ofgraduate course settings and a useful reference for practicingresearchers in the field.
Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.
* The classical principal components model and sample-populationinference
* Several extensions and modifications of principal components,including Q and three-mode analysis and principal components in thecomplex domain
* Maximum likelihood and weighted factor models, factoridentification, factor rotation, and the estimation of factorscores
* The use of factor models in conjunction with various types ofdata including time series, spatial data, rank orders, and nominalvariable
* Applications of factor models to the estimation of functionalforms and to least squares of regression estimators
"A must-have book for anyone expecting to do research and/orapplications in categorical data analysis."
—Statistics in Medicine
"It is a total delight reading this book."
"If you do any analysis of categorical data, this is anessential desktop reference."
The use of statistical methods for analyzing categorical datahas increased dramatically, particularly in the biomedical, socialsciences, and financial industries. Responding to new developments,this book offers a comprehensive treatment of the most importantmethods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial loglinear models for discrete datawith normal regression for continuous data. This edition alsofeatures:An emphasis on logistic and probit regression methods forbinary, ordinal, and nominal responses for independent observationsand for clustered data with marginal models and random effectsmodelsTwo new chapters on alternative methods for binary responsedata, including smoothing and regularization methods,classification methods such as linear discriminant analysis andclassification trees, and cluster analysisNew sections introducing the Bayesian approach for methods inthat chapterMore than 100 analyses of data sets and over 600 exercisesNotes at the end of each chapter that provide references torecent research and topics not covered in the text, linked to abibliography of more than 1,200 sourcesA supplementary website showing how to use R and SAS; for allexamples in the text, with information also about SPSS and Stataand with exercise solutions
Categorical Data Analysis, Third Edition is an invaluabletool for statisticians and methodologists, such as biostatisticiansand researchers in the social and behavioral sciences, medicine andpublic health, marketing, education, finance, biological andagricultural sciences, and industrial quality control.
Analyzing Microarray Gene Expression Data provides acomprehensive review of available methodologies for the analysis ofdata derived from the latest DNA microarray technologies. Designedfor biostatisticians entering the field of microarray analysis aswell as biologists seeking to more effectively analyze their ownexperimental data, the text features a unique interdisciplinaryapproach and a combined academic and practical perspective thatoffers readers the most complete and applied coverage of thesubject matter to date.
Following a basic overview of the biological and technicalprinciples behind microarray experimentation, the text provides alook at some of the most effective tools and procedures forachieving optimum reliability and reproducibility of researchresults, including:An in-depth account of the detection of genes that aredifferentially expressed across a number of classes of tissuesExtensive coverage of both cluster analysis and discriminantanalysis of microarray data and the growing applications of bothmethodologiesA model-based approach to cluster analysis, with emphasis onthe use of the EMMIX-GENE procedure for the clustering of tissuesamplesThe latest data cleaning and normalization proceduresThe uses of microarray expression data for providing importantprognostic information on the outcome of disease
When dealing with the topic of risk analysis, most books oninvestments treat downside and upside risk equally. Preparing forthe Worst takes an entirely novel approach by focusing on downsiderisk and explaining how to incorporate it into investmentdecisions. Highlighting this asymmetry of the stock market, theauthors describe how existing theories miss the downside and followwith explanations of how it can be included. Various techniques forcalculating downside risk are demonstrated.
This book presents the latest ideas in the field from the groundup, making the discussion accessible to mathematicians andstatisticians interested in applications in finance, as well as tofinance professionals who may not have a mathematical background.An invaluable resource for anyone wishing to explore the criticalissues of finance, portfolio management, and securities pricing,this book:Incorporates Value at Risk into the theoretical discussionUses many examples to illustrate downside risk in U.S.,international, and emerging market investmentsAddresses downside risk arising from fraud and corruptionIncludes step-by-step instructions on how to implement themethods introduced in this bookOffers advice on how to avoid pitfalls in calculations andcomputer programmingProvides software use information and tips
Developed and refined over a period of twenty years, the materialin this book offers an especially lucid presentation of linearstatistical models. These models lead to what is usually called"multiple regression" or "analysis of variance" methodology, which,in turn, opens up a wide range of applications to the physical,biological, and social sciences, as well as to business,agriculture, and engineering. Unlike similar books on this topic,Linear Statistical Models emphasizes the geometry of vector spacesbecause of the intuitive insights this approach brings to anunderstanding of the theory. While the focus is on theory, examplesof applications, using the SAS and S-Plus packages, are included.Prerequisites include some familiarity with linear algebra, andprobability and statistics at the postcalculus level.
Major topics covered include:
* Methods of study of random vectors, including the multivariatenormal, chi-square, t and F distributions, central and noncentral
* The linear model and the basic theory of regression analysis andthe analysis of variance
* Multiple regression methods, including transformations, analysisof residuals, and asymptotic theory for regression analysis.Separate sections are devoted to robust methods and to thebootstrap.
* Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey,and Bechhofer
* Analysis of variance, with two- and three-way analysis ofvariance
* Random component models, nested designs, and balanced incompleteblock designs
* Analysis of frequency data through log-linear models, withemphasis on vector space viewpoint. This chapter alone issufficient for a course on the analysis of frequency data.
"Survey Errors and Survey Costs is a well-written,well-presented, and highly readable text that should be on everyerror-conscious statistician’s bookshelf. Any courses thatcover the theory and design of surveys should certainly have SurveyErrors and Survey Costs on their reading lists."
MEL, Aston University Science Park, UK
Review in The Statistician, Vol. 40, No. 3, 1991
"This volume is an extremely valuable contribution to surveymethodology. It has many virtues: First, it provides a framework inwhich survey errors can be segregated by sources. Second, Groveshas skillfully synthesized existing knowledge, bringing together inan easily accessible form empirical knowledge from a variety ofsources. Third, he has managed to integrate into a common frameworkthe contributions of several disciplines. For example, the work ofpsychometricians and cognitive psychologists is made relevant tothe research of econometricians as well as the field experience ofsociologists. Finally, but not least, Groves has managed to presentall this in a style that is accessible to a wide variety of readersranging from survey specialists to policymakers."
–Peter H. Rossi
University of Massachusetts at Amherst
Review in Journal of Official Statistics, January 1991
This book offers a practical, application-based approach toflowgraph models for time-to-event data. It clearly shows how thisinnovative new methodology can be used to analyze data fromsemi-Markov processes without prior knowledge of stochasticprocesses--opening the door to interesting applications in survivalanalysis and reliability as well as stochastic processes.
Unlike other books on multistate time-to-event data, this workemphasizes reliability and not just biostatistics, illustratingeach method with medical and engineering examples. It demonstrateshow flowgraphs bring together applied probability techniques andcombine them with data analysis and statistical methods to answerquestions of practical interest. Bayesian methods of data analysisare emphasized. Coverage includes:
* Clear instructions on how to model multistate time-to-event datausing flowgraph models
* An emphasis on computation, real data, and Bayesian methods forproblem solving
* Real-world examples for analyzing data from stochasticprocesses
* The use of flowgraph models to analyze complex stochasticnetworks
* Exercise sets to reinforce the practical approach of thisvolume
Flowgraph Models for Multistate Time-to-Event Data is an invaluableresource/reference for researchers in biostatistics/survivalanalysis, systems engineering, and in fields that use stochasticprocesses, including anthropology, biology, psychology, computerscience, and engineering.
Continuous Multivariate Distributions, Volume 1, Models andApplications, 2nd Edition by Samuel Kotz, N. Balakrishnan andNormal L. Johnson
Continuous Univariate Distributions, Volume 1, 2nd Editionby Samuel Kotz, N. Balakrishnan and Normal L. Johnson
Continuous Univariate Distributions, Volume 2, 2nd Editionby Samuel Kotz, N. Balakrishnan and Normal L. Johnson
Discrete Multivariate Distributions by Samuel Kotz, N.Balakrishnan and Normal L. Johnson
Univariate Discrete Distributions, 3rd Edition by SamuelKotz, N. Balakrishnan and Normal L. Johnson
Discover the latest advances in discrete distributionstheory
The Third Edition of the critically acclaimedUnivariate Discrete Distributions provides a self-contained,systematic treatment of the theory, derivation, and application ofprobability distributions for count data. Generalized zeta-functionand q-series distributions have been added and are covered indetail. New families of distributions, including Lagrangian-typedistributions, are integrated into this thoroughly revised andupdated text. Additional applications of univariate discretedistributions are explored to demonstrate the flexibility of thispowerful method.
A thorough survey of recent statistical literature drawsattention to many new distributions and results for the classicaldistributions. Approximately 450 new references along with severalnew sections are introduced to reflect the current literature andknowledge of discrete distributions.
Beginning with mathematical, probability, and statisticalfundamentals, the authors provide clear coverage of the key topicsin the field, including:Families of discrete distributionsBinomial distributionPoisson distributionNegative binomial distributionHypergeometric distributionsLogarithmic and Lagrangian distributionsMixture distributionsStopped-sum distributionsMatching, occupancy, runs, and q-series distributionsParametric regression models and miscellanea
Emphasis continues to be placed on the increasing relevance ofBayesian inference to discrete distribution, especially with regardto the binomial and Poisson distributions. New derivations ofdiscrete distributions via stochastic processes and random walksare introduced without unnecessarily complex discussions ofstochastic processes. Throughout the Third Edition, extensiveinformation has been added to reflect the new role ofcomputer-based applications.
With its thorough coverage and balanced presentation of theoryand application, this is an excellent and essential reference forstatisticians and mathematicians.
This innovative book sets forth and describes methods for theanalysis of longitudinaldata, emphasizing applications to problemsin the biomedical and behavioral sciences. Reflecting the growingimportance and use of longitudinal data across many areas ofresearch, the text is designed to help users of statistics betteranalyze and understand this type of data.
Much of the material from the book grew out of a course taught byDr. Hedeker on longitudinal data analysis. The material is,therefore, thoroughly classroom tested and includes a number offeatures designed to help readers better understand and apply thematerial. Statistical procedures featured within the textinclude:
* Repeated measures analysis of variance
* Multivariate analysis of variance for repeated measures
* Random-effects regression models (RRM)
* Covariance-pattern models
* Generalized-estimating equations (GEE) models
* Generalizations of RRM and GEE for categorical outcomes
Practical in their approach, the authors emphasize the applicationsof the methods, using real-world examples for illustration. Somesyntax examples are provided, although the authors do not generallyfocus on software in this book. Several datasets and computersyntax examples are posted on this title's companion Web site. Theauthors intend to keep the syntax examples current as new versionsof the software programs emerge.
This text is designed for both undergraduate and graduate coursesin longitudinal data analysis. Instructors can take advantage ofoverheads and additional course materials available online foradopters. Applied statisticians in biomedicine and the socialsciences can also use the book as a convenient reference.
The recent explosion in longitudinal data in the social scienceshighlights the need for this timely publication. Latent CurveModels: A Structural Equation Perspective provides an effectivetechnique to analyze latent curve models (LCMs). This type of datafeatures random intercepts and slopes that permit each case in asample to have a different trajectory over time. Furthermore,researchers can include variables to predict the parametersgoverning these trajectories.
The authors synthesize a vast amount of research and findingsand, at the same time, provide original results. The book analyzesLCMs from the perspective of structural equation models (SEMs) withlatent variables. While the authors discuss simple regression-basedprocedures that are useful in the early stages of LCMs, most of thepresentation uses SEMs as a driving tool. This cutting-edge workincludes some of the authors' recent work on the autoregressivelatent trajectory model, suggests new models for method factors inmultiple indicators, discusses repeated latent variable models, andestablishes the identification of a variety of LCMs.
This text has been thoroughly class-tested and makes extensiveuse of pedagogical tools to aid readers in mastering and applyingLCMs quickly and easily to their own data sets. Key featuresinclude:Chapter introductions and summaries that provide a quickoverview of highlightsEmpirical examples provided throughout that allow readers totest their newly found knowledge and discover practicalapplicationsConclusions at the end of each chapter that stress theessential points that readers need to understand for advancement tomore sophisticated topicsExtensive footnoting that points the way to the primaryliterature for more information on particular topics
With its emphasis on modeling and the use of numerous examples,this is an excellent book for graduate courses in latent trajectorymodels as well as a supplemental text for courses in structuralmodeling. This book is an excellent aid and reference forresearchers in quantitative social and behavioral sciences who needto analyze longitudinal data.
In the course of the past 100 years, economists and appliedstatisticians have developed a remarkably diverse variety of incomedistribution models, yet no single resource convincingly accountsfor all of these models, analyzing their strengths and weaknesses,similarities and differences. Statistical Size Distributions inEconomics and Actuarial Sciences is the first collection tosystematically investigate a wide variety of parametric models thatdeal with income, wealth, and related notions.
Christian Kleiber and Samuel Kotz survey, compliment, compare,and unify all of the disparate models of income distribution,highlighting at times a lack of coordination between them that canresult in unnecessary duplication. Considering models from eightlanguages and all continents, the authors discuss the social andeconomic implications of each as well as distributions of size ofloss in actuarial applications. Specific models coveredinclude:Pareto distributionsLognormal distributionsGamma-type size distributionsBeta-type size distributionsMiscellaneous size distributions
Three appendices provide brief biographies of some of theleading players along with the basic properties of each of thedistributions. Actuaries, economists, market researchers, socialscientists, and physicists interested in econophysics will findStatistical Size Distributions in Economics and Actuarial Sciencesto be a truly one-of-a-kind addition to the professionalliterature.
Exploratory Data Mining and Data Cleaning will serve as animportant reference for serious data analysts who need to analyzelarge amounts of unfamiliar data, managers of operations databases,and students in undergraduate or graduate level courses dealingwith large scale data analys is and data mining.
Reliability is one of the most important characteristicsdefining the quality of a product or system, both for themanufacturer and the purchaser. One achieves high reliabilitythrough careful monitoring of design, materials and other input,production, quality assurance efforts, ongoing maintenance, and avariety of related decisions and activities. All of these factorsmust be considered in determining the costs of production,purchase, and ownership of a product.
Case Studies in Reliability and Maintenance servesas a valuable addition to the current literature on the subject ofreliability by bridging the gap between theory and application.Conceived during the preparation of the editors' earlier work,Reliability: Modeling, Prediction, and Optimization (Wiley, 2000),this new volume features twenty-six actual case studies written bytop experts in their fields, each illustrating exactly howreliability models are applied.
A valuable companion book to Reliability: Modeling,Prediction, and Optimization, or any other textbook on thesubject, the book features:Case studies from fields such as aerospace, automotive, mining,electronics, power plants, dikes, computer software, weapons,photocopiers, industrial furnaces, granite building cladding,chemistry, and aircraft enginesA logical organization according to the life cycle of a productor systemA unified format of discussion enhanced by tools, techniques,and models for drawing one's own conclusionsPertinent exercises for reinforcement of ideas
Of equal value to both students of reliability theory as well asprofessionals in industry, Case Studies in Reliability andMaintenance should be required reading for anyone seekingto understand how reliability and maintenance issues can beaddressed and resolved in the real world.
Recent developments in computer technology have stimulated new andexciting uses for graphics in statistical analyses. RegressionGraphics, one of the first graduate-level textbooks on the subject,demonstrates how statisticians, both theoretical and applied, canuse these exciting innovations. After developing a relatively newregression context that requires few scope-limiting conditions,Regression Graphics guides readers through the process of analyzingregressions graphically and assessing and selecting models. Thisinnovative reference makes use of a wide range of graphical tools,including 2D and 3D scatterplots, 3D binary response plots, andscatterplot matrices. Supplemented by a companion ftp site, itfeatures numerous data sets and applied examples that are used toelucidate the theory.
Other important features of this book include:
* Extensive coverage of a relatively new regression context basedon dimension-reduction subspaces and sufficient summary plots
* Graphical regression, an iterative visualization process forconstructing sufficient regression views
* Graphics for regressions with a binary response
* Graphics for model assessment, including residual plots
* Net-effects plots for assessing predictor contributions
* Graphics for predictor and response transformations
* Inverse regression methods
* Access to a Web site of supplemental plots, data sets, and 3Dcolor displays.
An ideal text for students in graduate-level courses on statisticalanalysis, Regression Graphics is also an excellent reference forprofessional statisticians.
* Full set of exercises is displayed at the end of eachchapter.
* First seven chapters cover standard topics in time series at ahigh-intensity level.
* Recent and timely developments in nonstandard time seriestechniques are illustrated with real finance examples indetail.
* Examples are systemically illustrated with S-plus with codes anddata available on an associated Web site.
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Regression methods have been an integral part of time seriesanalysis for over a century. Recently, new developments have mademajor strides in such areas as non-continuous data where a linearmodel is not appropriate. This book introduces the reader to newerdevelopments and more diverse regression models and methods fortime series analysis.
Accessible to anyone who is familiar with the basic modern conceptsof statistical inference, Regression Models for Time SeriesAnalysis provides a much-needed examination of recent statisticaldevelopments. Primary among them is the important class of modelsknown as generalized linear models (GLM) which provides, under someconditions, a unified regression theory suitable for continuous,categorical, and count data.
The authors extend GLM methodology systematically to time serieswhere the primary and covariate data are both random andstochastically dependent. They introduce readers to variousregression models developed during the last thirty years or so andsummarize classical and more recent results concerning state spacemodels. To conclude, they present a Bayesian approach to predictionand interpolation in spatial data adapted to time series that maybe short and/or observed irregularly. Real data applications andfurther results are presented throughout by means of chapterproblems and complements.
Notably, the book covers:
* Important recent developments in Kalman filtering, dynamic GLMs,and state-space modeling
* Associated computational issues such as Markov chain, MonteCarlo, and the EM-algorithm
* Prediction and interpolation
* Stationary processes
Numerical Methods in Finance bridges the gap between financialtheory and computational practice while helping students andpractitioners exploit MATLAB for financial applications.
Paolo Brandimarte covers the basics of finance and numericalanalysis and provides background material that suits the needs ofstudents from both financial engineering and economicsperspectives. Classical numerical analysis methods; optimization,including less familiar topics such as stochastic and integerprogramming; simulation, including low discrepancy sequences; andpartial differential equations are covered in detail. Extensiveillustrative examples of the application of all of thesemethodologies are also provided.
The text is primarily focused on MATLAB-based application, but alsoincludes descriptions of other readily available toolboxes that arerelevant to finance. Helpful appendices on the basics of MATLAB andprobability theory round out this balanced coverage. Accessible forstudents-yet still a useful reference for practitioners-NumericalMethods in Finance offers an expert introduction to powerful toolsin finance.